Date: 21/04/16
Author: Jonathan Chan
Description: Data Analysis on Retail Data
Initial Data Loading
library("tidyverse")
library("lubridate")
library("ggmap")
library("plotly")
library("viridis")
library("wesanderson")
library("DT")
Initialising data, which includes original data and new data, joining them to create a master set.
info <- read.table("./Data/All_info.txt", header = TRUE, na.strings = c("", "NA"),
stringsAsFactors = FALSE)
info_new <- read.csv("./Data/All_info(new)-1.csv", stringsAsFactors = FALSE, header = TRUE,
na.strings = c("", "NA"))
colnames(info_new) <- colnames(info)
info_temp <- rbind(info, info_new)
info_walk <- read.csv("./Data/info walk-1.csv", stringsAsFactors = FALSE)
Overall there are 6429 rows of data. With 1173 unique customers.
Converts the dates in character format to date format and calculate age from date of birth. Redacted a lot of columns
info_merge <- info_temp
info_merge$Order.date <- dmy(info_temp$Order.date)
info_merge$D.O.B. <- dmy(info_temp$D.O.B.)
info_merge <- info_merge %>% mutate(Age = year(as.period(interval(info_merge$D.O.B., "21/04/16"))))
select(info_merge[45:54, ], -c(Name, Post, D.O.B., Age, Sex, Gross.profit, Amount.1, Value, Order.value, Net.Order.Value, Proj..Value.w.o.discount, Proj..Cost.of.sales, Amount))
Category Analysis
To start of with some data cleansing to merge different type of horn products into a single group.
info_merge$Material <- gsub(pattern = "Buffalo Horn", replace = "Horn", x = info_merge$Material)
info_merge$Material <- gsub(pattern = "Carbon Horn", replace = "Horn", x = info_merge$Material)
info_merge$Material <- gsub(pattern = "Thin Horn", replace = "Horn", x = info_merge$Material)
By Material
Material with Age
material_plot <- ggplot(filter(info_merge, Age > 0 & Age < 100,
Material %in% c("Acetate", "Horn", "Combination", "Titanium", "Rimless")),
aes(x = Material, y = Age, fill = Material)) +
theme(axis.text.y=element_blank()) +
geom_boxplot() +
geom_jitter(alpha = 0.2) +
scale_fill_viridis(discrete=T) +
geom_violin(alpha = 0.25)
ggtitle("Frame Material with Age")
$`title`
[1] "Frame Material with Age"
$subtitle
NULL
attr(,"class")
[1] "labels"
# ggplotly(material_plot, width = 900)
material_plot

mat.type <- data.frame()
mat.type <- info_merge %>%
filter(Product.type %in% c("Frames", "Sunglasses"),
Material %in% c("Acetate", "Horn", "Combination", "Titanium", "Rimless"),
Amount > 0, # takes only frames made by TD
is.na(Sex) == FALSE) %>%
group_by(Product.type, Material, Sex) %>%
summarise(orders = n(), Total.Order.value = sum(Amount, na.rm = TRUE), no.customers = n_distinct(Name))
mat.type <- mutate(mat.type,
Percentage.Orders = orders/sum(mat.type$orders), # Creates profit/order and % of orders
Percentage.Order.Value = Total.Order.value/sum(mat.type$Total.Order.value),
Average.Order.value = Total.Order.value/orders,
Order.customer.ratio = orders/no.customers)
Lens Analysis
Lens_type <- info_merge %>%
filter(Product.type == "Lenses") %>%
group_by(Lens.type) %>%
summarise(number = n())
Lens Type with Age
Lens_data <- subset(info_merge, Age > 0 & Age < 100 & Lens.type != "Bifocal")
Lens_data$Lens.type <- factor(Lens_data$Lens.type, levels = c("Single vision", "Progressive"))
# ggplotly(
ggplot(Lens_data, aes(x = Lens.type, y = Age)) +
geom_boxplot() +
theme(axis.text.y=element_blank()) +
geom_jitter(alpha = 0.2) +
geom_violin(alpha = 0.25)#,

# width = 900
# )
Lens Coatings/Finishes by Age

Age Patterns
Age distribution
customers_age <- info_merge %>% filter(Age < 100, Age > 0, Amount > 0)
# Histogram of frequency of customers of certain age
# ggplotly(
ggplot(customers_age %>% distinct(Name, Age), aes(Age)) +
theme(axis.text.y=element_blank(), axis.text.x=element_blank()) +
geom_histogram(binwidth = 5, alpha = 0.8, col = "white") +
scale_x_continuous(breaks = seq(0, 100, 5))#,

# width = 900
# )
Total Spend against Age (For Each Customer)
Total_spend <- customers_age %>%
group_by(Name) %>%
summarise(Total.Spend = sum(Amount), Age = mean(Age))
# ggplotly(
ggplot(Total_spend, aes(x = Age, y = Total.Spend)) +
theme(axis.text.y=element_blank()) +
geom_point(shape = 1) +
geom_smooth() +
scale_x_continuous(breaks = seq(0, 100, 5)) +
scale_y_continuous(breaks = seq(0, 10000, 1000))#,

# width = 900
# )
Average Total Spend by Age
Regression Plot
Average_spend <- customers_age %>%
group_by(Name) %>%
summarise(Total.Spend = sum(Amount), Age = mean(Age)) %>%
group_by(Age) %>%
summarise(Average.spend = mean(Total.Spend))
# ggplotly(
ggplot(Average_spend, aes(x = Age, y = Average.spend)) +
theme(axis.text.y=element_blank()) +
geom_point(shape = 1) +
geom_smooth(method = "lm", formula = y ~ poly(x, 3)) +
scale_x_continuous(breaks = seq(0, 100, 10)) +
scale_y_continuous(breaks = seq(0, 10000, 100)) +
ggtitle("Regression of Average Spend with Age (3rd Order Polynomial)")#,

# width = 900
# )
Diagnostic Plots
Redacted
Frame cost and age
Product_age <- info_merge %>%
filter(Product.type %in% c("Frames", "Sunglasses"), Amount > 0, Age > 0, Age < 100, Amount < 2500)
# ggplotly(
ggplot(Product_age, aes(x = Age, y = Amount)) +
theme(axis.text.y=element_blank()) +
geom_point(shape = 1) +
geom_smooth()#,

# width = 900
# )
Customer Age Group - Customer Count and Spend

Purchase Frequency with Age

Visit Statistics
All charts are redacted ## Total number of purchases per customer
Total number of visits per customer
Number of purchases per visit
Number of Days Between Visits

Cumulative Graph of Number of Days to Customer Next Visit
If a customer visits again, 60% of the time a customer they will return within the first 90 days since their last visit.
ggplotly(
ggplot(visits %>% arrange(days_next_visit), aes(days_next_visit)) +
stat_ecdf(geom = "step", col = "blue", size = 0.5) +
scale_x_continuous(breaks = seq(0, 600, 30)) +
scale_y_continuous(breaks = seq(0, 1, 0.1)) +
ylab("Percentage"),
width = 900
)
Removed 903 rows containing non-finite values (stat_ecdf).
Customer ranking and percentile calculations
Note that table data has been redacted
Sales with Time
individual sales with time
# ggplotly(
ggplot(info_merge %>% filter(Amount > 0), aes(x = Order.date, y = Amount)) +
theme(axis.text.y=element_blank()) +
geom_point(shape = 1) +
geom_smooth()#,

# width = 900
# )
total daily sales with time
Sales_by_date <- info_merge %>%
group_by(Date = Order.date) %>%
summarise(Sales = sum(Amount, na.rm = TRUE))
# ggplotly(
ggplot(Sales_by_date, aes(x = Date, y = Sales)) +
theme(axis.text.y=element_blank()) +
geom_point(shape = 1) +
geom_smooth()#,

# width = 900
# )
total monthly sales with time
Monthly sales appear to rise and then tapper off, but the time frame is short so it might be a blip.
Sales_by_month <- info_merge %>%
mutate(Month = floor_date(Order.date, "month")) %>%
group_by(Month) %>%
summarise(month_sales = sum(Amount, na.rm = T))
# September sales is incomplete so excluded in this case
# ggplotly(
ggplot(Sales_by_month, aes(x = Month, y = month_sales, ymin = 0)) +
theme(axis.text.y=element_blank()) +
geom_point(size = 2) +
geom_smooth()

# width = 900
# )
unique customers plot earliest purchase date
Drop in sales might be due to drop in new customer acquisition rate.

Customer location mapping
post_codes <- read.csv("./Data/Postcodes.csv")
post_codes <- post_codes[ ,1]
coordinates <- read.csv("Postcodes coordinates.csv")
## install for newest verion of ggmap
#if(!requireNamespace("devtools")) install.packages("devtools")
#devtools::install_github("dkahle/ggmap", ref = "tidyup")
register_google(key = "AIzaSyD88nanfnGGg2iH5GqWsnznlNgad3SiBd4")
UK
90% of customers are located in the UK
ggmap(get_map("Machester", zoom = 6, api_key = key), extent = "device") +
geom_point(aes(x = lon, y = lat), data = coordinates,
alpha = .15, color="darkred", size = 2)
Source : https://maps.googleapis.com/maps/api/staticmap?center=Machester&zoom=6&size=640x640&scale=2&maptype=terrain&language=en-EN&key=xxx
Source : https://maps.googleapis.com/maps/api/geocode/json?address=Machester&key=xxx

Greater London
60% of all customers are located in London
ggmap(get_map("SW1W", zoom = 11, api_key = key), extent = "device") +
geom_point(aes(x = lon, y = lat), data = coordinates,
alpha = .15, color="darkred", size = 2)
Source : https://maps.googleapis.com/maps/api/staticmap?center=SW1W&zoom=11&size=640x640&scale=2&maptype=terrain&language=en-EN&key=xxx
Source : https://maps.googleapis.com/maps/api/geocode/json?address=SW1W&key=xxx

London
ggmap(get_map("SW1W", zoom = 12, api_key = key), extent = "device")+
geom_point(aes(x = lon, y = lat), data = coordinates,
alpha = .2, color="darkred", size = 2)
Source : https://maps.googleapis.com/maps/api/staticmap?center=SW1W&zoom=12&size=640x640&scale=2&maptype=terrain&language=en-EN&key=xxx
Source : https://maps.googleapis.com/maps/api/geocode/json?address=SW1W&key=xxx

London 2
ggmap(get_map("SW1W", zoom = 13, api_key = key), extent = "device")+
geom_point(aes(x = lon, y = lat), data = coordinates,
alpha = .25, color="darkred", size = 2)
Source : https://maps.googleapis.com/maps/api/staticmap?center=SW1W&zoom=13&size=640x640&scale=2&maptype=terrain&language=en-EN&key=xxx
Source : https://maps.googleapis.com/maps/api/geocode/json?address=SW1W&key=xxx

Sloane Square
ggmap(get_map("Sloane Square", zoom = 14, api_key = key), extent = "device")+
geom_point(aes(x = lon, y = lat), data = coordinates,
alpha = .25, color="darkred", size = 2)
Source : https://maps.googleapis.com/maps/api/staticmap?center=Sloane%20Square&zoom=14&size=640x640&scale=2&maptype=terrain&language=en-EN&key=xxx
Source : https://maps.googleapis.com/maps/api/geocode/json?address=Sloane%20Square&key=xxx

Walking Distance from Store
distance <- data.frame()
# $V1 takes it as a vector instead of data frame
London_regions <- read.csv("./Data/London Town and Boroughs.csv", stringsAsFactors = FALSE, header = FALSE)$V1
Post_code_regex <- "^([A-PR-UWYZ0-9][A-HK-Y0-9][AEHMNPRTVXY0-9]?[ABEHMNPRVWXY0-9]? {1,2}[0-9][ABD-HJLN-UW-Z]{2}|GIR 0AA)$"
London_cust <- info %>%
filter(grepl(Post_code_regex, Post), tolower(Town) %in% tolower(London_regions), Amount > 0) %>%
distinct(Post)
Histogram of Walking Distance
# ggplotly(
ggplot(walking_dist, aes(minutes)) +
theme(axis.text.y=element_blank()) +
geom_histogram(position = 'stack', binwidth = 5, alpha = 0.9, col = "white", boundary = 0)#,

# width = 900
# )
Cumulative Graph of Walking Distance from Store
Only 30% of customers live within a 30 minute walk from the store. Some may work nearby, or the store may be a destination store instead of relying on local clientel.
ggplotly(
ggplot(walking_dist %>% arrange(minutes), aes(minutes)) +
stat_ecdf(geom = "step", col = "blue", size = 0.5) +
scale_x_continuous(breaks = seq(0, 300, 15)) +
scale_y_continuous(breaks = seq(0, 1, 0.1)) +
ylab("Percentage") +
ggtitle("Walking Distance from Store"),
width = 900
)
Age Demographics Comparison
# Categorises customer numbers and spending into age categories and filters by proximity of 45 min walk
age_summary <- function(lower, upper){
info_walk %>%
group_by(Name) %>%
summarise(spend = sum(Amount, na.rm = TRUE), Age = Age[1], minutes = minutes[1]) %>% # as each name has multiple only take 1st age&min
filter(Age >= lower, Age <= upper, spend > 0, minutes > 30) %>%
summarise(count = n_distinct(Name))
}
# Census data
census <- read.csv("./Census data/Census area summary.csv", stringsAsFactors = FALSE)
TD_count <- mapply(age_summary, lower = census$Lower, upper = census$Upper)
TD_count <- matrix(unlist(t(TD_count)), ncol = 1) #Transposes then converts the list to a matrix
colnames(TD_count) <- "TD_count"
census_sum <- cbind(census, TD_count)
census_sum <- mutate(census_sum, count_percent = TD_count/sum(TD_count))
age_group <- paste(census_sum[ ,1], "-", census_sum[ ,2], sep = "")
comparison <- cbind(age_group, census_sum[,3:6])
colnames(comparison) <- c("age_group", "Count", "Census", "TD_count", "TD customers")
comp_long <- cbind(age_group, gather(comparison[,2:5]))
comp_long <- subset(comp_long, key %in% c("Census", "TD customers"))
# keep data as factors, otherwise it sorts by numerical order
comp_long$age_group <- factor(comp_long$age_group, levels = unique(comp_long$age_group))
comp_long$key <- factor(comp_long$key, levels = unique(comp_long$key))
For age comparison it only looks at 160 out of 940 customer, after filtering for 45min walking proximity, age data and spending. Overall compared to the local area the store attracts a much larger proportion of those aged between 45-59 compared to other ages groups.
comp_long <- comp_long %>% mutate(percent = value*100)
# ggplotly(
ggplot(comp_long, aes(x = age_group, y = percent, fill = key)) +
theme(axis.text.x=element_blank()) +
geom_bar(stat = "identity", position = position_dodge()) +
xlab("Age Categories") +
ylab("Percentage") +
ggtitle("Comparison of Age Groups (2011 K&C Census vs Store)") +
scale_y_continuous(breaks = seq(0, 100, 10))#,

# width = 900,
# height = 500
# )
---
title: "Data Analysis on Retail Data"
output:
  html_notebook:
    number_sections: yes
    toc: yes
    toc_float: yes
  pdf_document:
    toc: yes
  html_document:
    df_print: paged
    toc: yes
---
   
Date: 21/04/16   
Author: Jonathan Chan   
Description: Data Analysis on Retail Data   

# Initial Data Loading

```{r message=FALSE, warning=FALSE}
library("tidyverse")
library("lubridate")
library("ggmap")
library("plotly")
library("viridis")
library("wesanderson")
library("DT")
```

Initialising data, which includes original data and new data, joining them to create a master set.
```{r}
info <- read.table("./Data/All_info.txt", header = TRUE, na.strings = c("", "NA"), 
                   stringsAsFactors = FALSE)
info_new <- read.csv("./Data/All_info(new)-1.csv", stringsAsFactors = FALSE, header = TRUE,
                     na.strings = c("", "NA"))
colnames(info_new) <- colnames(info)
info_temp <- rbind(info, info_new)

info_walk <- read.csv("./Data/info walk-1.csv", stringsAsFactors = FALSE)
```
Overall there are `r nrow(info_merge)` rows of data. With `r n_distinct(info_merge$Name)` unique customers.   

Converts the dates in character format to date format and calculate age from date of birth.
Redacted a lot of columns
```{r}
info_merge <- info_temp
info_merge$Order.date <- dmy(info_temp$Order.date)
info_merge$D.O.B. <- dmy(info_temp$D.O.B.)
info_merge <- info_merge %>% mutate(Age = year(as.period(interval(info_merge$D.O.B., "21/04/16"))))
select(info_merge[45:54, ], -c(Name, Post, D.O.B., Age, Sex, Gross.profit, Amount.1, Value, Order.value, Net.Order.Value, Proj..Value.w.o.discount, Proj..Cost.of.sales, Amount))
```

# Category Analysis
To start of with some data cleansing to merge different type of horn products into a single group.
```{r}
info_merge$Material <- gsub(pattern = "Buffalo Horn", replace = "Horn", x = info_merge$Material)
info_merge$Material <- gsub(pattern = "Carbon Horn", replace = "Horn", x = info_merge$Material)
info_merge$Material <- gsub(pattern = "Thin Horn", replace = "Horn", x = info_merge$Material)
```
## By Material
### Material with Age
```{r}
material_plot <- ggplot(filter(info_merge, Age > 0 & Age < 100, 
              Material %in% c("Acetate", "Horn", "Combination", "Titanium", "Rimless")), 
             aes(x = Material, y = Age, fill = Material)) +
  theme(axis.text.y=element_blank()) +
  geom_boxplot() +
  geom_jitter(alpha = 0.2) +
  scale_fill_viridis(discrete=T) +
  geom_violin(alpha = 0.25)
  ggtitle("Frame Material with Age")

# ggplotly(material_plot, width = 900)
  material_plot
```
```{r}
mat.type <- data.frame()
mat.type <- info_merge %>%
  filter(Product.type %in% c("Frames", "Sunglasses"),
         Material %in% c("Acetate", "Horn", "Combination", "Titanium", "Rimless"),
         Amount > 0,  # takes only frames made by TD
         is.na(Sex) == FALSE) %>%
  group_by(Product.type, Material, Sex) %>%
  summarise(orders = n(), Total.Order.value = sum(Amount, na.rm = TRUE), no.customers = n_distinct(Name))

mat.type <- mutate(mat.type, 
              Percentage.Orders = orders/sum(mat.type$orders),  # Creates profit/order and % of orders
              Percentage.Order.Value = Total.Order.value/sum(mat.type$Total.Order.value),
              Average.Order.value = Total.Order.value/orders,
              Order.customer.ratio = orders/no.customers)

```
## Lens Analysis
```{r}
Lens_type <- info_merge %>% 
  filter(Product.type == "Lenses") %>%
  group_by(Lens.type) %>%
  summarise(number = n())
```

### Lens Type with Age
```{r}
Lens_data <- subset(info_merge, Age > 0 & Age < 100 & Lens.type != "Bifocal")
Lens_data$Lens.type <- factor(Lens_data$Lens.type, levels = c("Single vision", "Progressive"))
# ggplotly(
  ggplot(Lens_data, aes(x = Lens.type, y = Age)) +
    geom_boxplot() +
    theme(axis.text.y=element_blank()) +
    geom_jitter(alpha = 0.2) +
    geom_violin(alpha = 0.25)#,
#   width = 900
# )
```

### Lens Coatings/Finishes by Age
```{r}
Finish_type <- info_merge %>% 
  filter(Product.type == "Lenses") %>%
  group_by(Finish) %>%
  summarise(number = n())
write.csv(Finish_type, "finish.csv", row.names = FALSE)

Lens_types <- read.csv("Lens type.csv")
info_lens <- merge(x = info_merge, y = Lens_types, by.x = "Finish", by.y = "Finish")
info_lens <- filter(info_lens, Age < 100, Age > 0)
info_lens$Type <- factor(info_lens$Type, levels = c("Single Vision", "Digital", "Office Lens", "Progressive"))

# ggplotly(
  ggplot(info_lens, aes(x = Type, y = Age, fill = Type)) +
    geom_boxplot() +
    theme(axis.text.y=element_blank()) +
    geom_jitter(alpha = 0.2) +
    geom_violin(alpha = 0.25) +
    scale_fill_manual(values = wes_palette("Moonrise3", 4, type = "discrete"))#,
#   width = 900
# )
```

# Age Patterns
## Age distribution
```{r}
customers_age <- info_merge %>% filter(Age < 100, Age > 0, Amount > 0)

#  Histogram of frequency of customers of certain age
# ggplotly(
  ggplot(customers_age %>% distinct(Name, Age), aes(Age)) +
        theme(axis.text.y=element_blank(), axis.text.x=element_blank()) +
        geom_histogram(binwidth = 5, alpha = 0.8, col = "white") +
        scale_x_continuous(breaks = seq(0, 100, 5))#,
#   width = 900
# )
```

## Total Spend against Age (For Each Customer)
```{r}
Total_spend <- customers_age %>% 
                group_by(Name) %>% 
                summarise(Total.Spend = sum(Amount), Age = mean(Age))
# ggplotly(
  ggplot(Total_spend, aes(x = Age, y = Total.Spend)) + 
    theme(axis.text.y=element_blank()) +
    geom_point(shape = 1) +
    geom_smooth() +
    scale_x_continuous(breaks = seq(0, 100, 5)) +
    scale_y_continuous(breaks = seq(0, 10000, 1000))#,
#   width = 900
# )
```

## Average Total Spend by Age {.tabset}
### Regression Plot
```{r}
Average_spend <- customers_age %>% 
  group_by(Name) %>% 
  summarise(Total.Spend = sum(Amount), Age = mean(Age)) %>%
  group_by(Age) %>% 
  summarise(Average.spend = mean(Total.Spend))

# ggplotly(
  ggplot(Average_spend, aes(x = Age, y = Average.spend)) + 
    theme(axis.text.y=element_blank()) +
    geom_point(shape = 1) +
    geom_smooth(method = "lm", formula = y ~ poly(x, 3)) +
    scale_x_continuous(breaks = seq(0, 100, 10)) +
    scale_y_continuous(breaks = seq(0, 10000, 100)) + 
    ggtitle("Regression of Average Spend with Age (3rd Order Polynomial)")#,
#   width = 900
# )
```
### Model Summary
Redacted
```{r include=FALSE}
age_spending <- lm(Average_spend$Average.spend ~ poly(Average_spend$Age, 3))

summary(age_spending)
```
### Diagnostic Plots
Redacted
```{r include=FALSE}
par(mfrow = c(2, 2))
plot(age_spending)
```


## Frame cost and age
```{r}
Product_age <- info_merge %>%
  filter(Product.type %in% c("Frames", "Sunglasses"), Amount > 0, Age > 0, Age < 100, Amount < 2500)
# ggplotly(
  ggplot(Product_age, aes(x = Age, y = Amount)) + 
    theme(axis.text.y=element_blank()) +
    geom_point(shape = 1) +
    geom_smooth()#,
#   width = 900
# )
```

## Customer Age Group - Customer Count and Spend
```{r}
Age_range <- cbind(seq(1, 81, 5), seq(5, 85, 5))
Age_label <- paste(Age_range[,1], "-", Age_range[,2], sep = "")
info_merge$Age_group <- NA

info_merge$Age_group <- cut(info_merge$Age, breaks = seq(0, 85, 5), labels = Age_label)

age_grouping <- info_merge %>%
  filter(Amount > 0, Age_group != "NA") %>%
  group_by(Age_group) %>%
  summarise(Count = n_distinct(Name), Total_amount = sum(Amount, na.rm = TRUE)) %>%
  mutate(per_count = Count/sum(Count)*100, per_amount = Total_amount/sum(Total_amount)*100)

info_merge %>%
  filter(Amount > 0) %>%
  count(Age_group)

rank_p2 <- info_merge %>% 
  filter(Amount > 0) %>%
  group_by(Name) %>%
  summarise(Total.Spend = sum(Amount, na.rm = TRUE), Age = mean(Age))
  rank_p2 %>% mutate(age_group = cut(rank_p2$Age, breaks = seq(0, 85, 5), labels = Age_label)) %>%
  count(age_group)

    
age_grouping2 <- age_grouping[, c("Age_group", "per_count", "per_amount")]
colnames(age_grouping2) <- c("Age.Group", "Number", "Spend")
age_grouping2 <- gather(age_grouping2, "Type", "Percent", Number, Spend)

age_plot <- ggplot(age_grouping2, aes(x = Age.Group, y = Percent, group = Type, col = Type, fill = Type)) +
  geom_bar(stat = "identity", alpha = 0.8, position = position_dodge()) +
  theme(axis.text.x=element_blank()) +
  scale_y_continuous(breaks = seq(0, 25, 5)) +
  ggtitle("Age Group Breakdown") +
  ylab("Percent") +
  xlab("Age Group")

# ggplotly(age_plot, width = 900)
age_plot
```
## Purchase Frequency with Age
```{r}
# Looks at frequency of transactions based on age
count_age <- info_merge %>% 
  group_by(Name) %>% 
  summarise(No.orders = n_distinct(Order.number), Age = mean(Age)) %>%
  group_by(Age) %>% 
  summarise(count = n()) %>% 
  filter(count > 2, Age < 100, Age > 0)
# average number of orders based on age
freq_age <- info_merge %>% 
  group_by(Name) %>% 
  summarise(No.orders = n_distinct(Order.number), Age = mean(Age)) %>%
  group_by(Age) %>%
  filter(Age %in% count_age$Age, Age != 1) %>%
  summarise(average = mean(No.orders))
# plots the above average numeber of purchases bsed on age
# ggplotly(
  ggplot(freq_age, aes(Age, average)) +
    theme(axis.text.y=element_blank()) +
    geom_point() +
    geom_smooth() +
    scale_x_continuous(breaks = seq(0, 100, 10)) +
    ylab("Number of Purchases")#,
#   width = 900
# )
```
# Visit Statistics
All charts are redacted
## Total number of purchases per customer
```{r include=FALSE}
# looks at the frequency of purchases & visits
freq_purchase_visits <- info_merge %>% 
  filter(Amount > 0, Product.type %in% c("Sunglasses", "Frames")) %>%
  group_by(Name) %>%
  summarise(purchases = n(), visits = n_distinct(Order.date)) 
purchase_number <- freq_purchase_visits %>% count(purchases)

plot_ly(purchase_number, labels = ~purchases, values = ~n, type = "pie", width = 900)
```
## Total number of visits per customer
```{r include=FALSE}
# same but for vists frequency
freq_visits <- freq_purchase_visits %>% count(visits)

plot_ly(freq_visits, labels = ~visits, values = ~n, type = "pie", width = 900)
```

## Number of purchases per visit
```{r include=FALSE}
# number of purchases on same day by visit, frames only
no_purchase_day <- info_merge %>% 
  filter(Amount > 0, Product.type %in% c("Sunglasses", "Frames")) %>%
  group_by(Name, Order.date) %>% 
  summarise(purchases = n()) %>%
  count(purchases) %>%
  mutate(percentage = n/sum(n)*100)

plot_ly(no_purchase_day, labels = ~purchases, values = ~n, type = "pie", width = 900)
```
## Number of Days Between Visits
```{r}
multi_buy_visit <- info_merge %>% 
  filter(Amount > 0, Product.type %in% c("Sunglasses", "Frames")) %>%
  group_by(Name, Order.date) %>% 
  summarise(purchases = n()) %>% 
  count(purchases)

# calculates the number of days between the order date and the last order date
visits <- info_merge %>% 
  filter(Product.type %in% c("Frames", "Sunglasses"), Amount > 0) %>%
  group_by(Name) %>%
  distinct(Order.date) %>%
  arrange(Name, Order.date) %>%
  mutate(days_next_visit = Order.date - lag(Order.date))

# plots the number of days until their next visit
# ggplotly(
  ggplot(visits, aes(days_next_visit)) +
    geom_histogram(binwidth = 30, alpha = 0.75, col = "white", fill = "Dark Blue", boundary=0)+
    theme(axis.text.y=element_blank()) +
    scale_x_continuous(breaks = seq(0, 600, 30)) +
    ggtitle("Days to Next Visit") +
    xlab("Number of Days") +
    ylab("Number of Visits")#,
#   width = 900
# )
```
## Cumulative Graph of Number of Days to Customer Next Visit
If a customer visits again, 60% of the time a customer they will return within the first 90 days since their last visit.
```{r}
ggplotly(
  ggplot(visits %>% arrange(days_next_visit), aes(days_next_visit)) + 
    stat_ecdf(geom = "step", col = "blue", size = 0.5) +
    scale_x_continuous(breaks = seq(0, 600, 30)) +
    scale_y_continuous(breaks = seq(0, 1, 0.1)) +
    ylab("Percentage"),
  width = 900
)
```
## Customer ranking and percentile calculations
Note that table data has been redacted
```{r}

percentile_label <- paste(seq(0, 95, 5), "-", seq(5, 100, 5), sep ="")
Age_range <- cbind(seq(1, 81, 5), seq(5, 85, 5))
Age_label <- paste(Age_range[,1], "-", Age_range[,2], sep = "")

rank_p1 <- info_merge %>% 
  filter(Amount > 0, Product.type %in% c("Sunglasses", "Frames")) %>%
  group_by(Name) %>%
  summarise(Frames.Bought = n(), Visits = n_distinct(Order.date))
rank_p2 <- info_merge %>% 
  filter(Amount > 0) %>%
  group_by(Name) %>%
  summarise(Total.Spend = sum(Amount, na.rm = TRUE), Age = mean(Age))
ranking <- merge(rank_p1, rank_p2, all.y = TRUE)

ranking <- ranking %>% 
  mutate(Ranking = rank(-Total.Spend, ties.method = "min"), 
         Percentile =Ranking/max(Ranking)*100, 
         Average_Frame_Spend = Total.Spend/Frames.Bought, 
         Average_Visit_Spend = Total.Spend/Visits)

ranking <- ranking %>% 
  mutate(percentile_group = cut(ranking$Percentile, breaks = seq(0, 100, 5), 
                                labels = percentile_label), 
         age_group = cut(ranking$Age, breaks = seq(0, 85, 5), labels = Age_label))

write.csv(ranking, "Ranking.csv", row.names = FALSE)
head(select(ranking, -c(Name, Age, Total.Spend, Average_Frame_Spend, Average_Visit_Spend)), 9)
```
# Sales with Time
## individual sales with time
```{r}
# ggplotly(
  ggplot(info_merge %>% filter(Amount > 0), aes(x = Order.date, y = Amount)) +
    theme(axis.text.y=element_blank()) +
    geom_point(shape = 1) +
    geom_smooth()#,
#   width = 900
# )
```

## total daily sales with time
```{r}
Sales_by_date <- info_merge %>% 
                  group_by(Date = Order.date) %>% 
                  summarise(Sales = sum(Amount, na.rm = TRUE))
# ggplotly(
  ggplot(Sales_by_date, aes(x = Date, y = Sales)) +
    theme(axis.text.y=element_blank()) +
    geom_point(shape = 1) +
    geom_smooth()#,
#   width = 900
# )
```

## total monthly sales with time
Monthly sales appear to rise and then tapper off, but the time frame is short so it might be a blip.
```{r}
Sales_by_month <- info_merge %>% 
  mutate(Month = floor_date(Order.date, "month")) %>%
  group_by(Month) %>% 
  summarise(month_sales = sum(Amount, na.rm = T))

# September sales is incomplete so excluded in this case
# ggplotly(
  ggplot(Sales_by_month, aes(x = Month, y = month_sales, ymin = 0)) +  
    theme(axis.text.y=element_blank()) +
    geom_point(size = 2) +
    geom_smooth()#,
#   width = 900
# )
```

##unique customers plot earliest purchase date
Drop in sales might be due to drop in new customer acquisition rate. 
```{r}
new_customer <- info_merge %>% 
  filter(Amount > 0) %>%
  group_by(Name) %>%
  summarise(earliest.order = min(Order.date)) %>%
  group_by(Month = floor_date(earliest.order, "month")) %>% 
  summarise(new.customers = n())

summary(new_customer$new.customers)

# ggplotly(
  ggplot(new_customer, aes(x = Month, y = new.customers, ymin = 0)) +
    theme(axis.text.y=element_blank()) +
    geom_point(size = 3) +
    geom_smooth() +
    geom_hline(yintercept = mean(new_customer$new.customers), color = "green") +
    ggtitle("New Customer Acqusition Rate")#,
#   width = 900
# )
```

# Customer location mapping {.tabset}
```{r}
post_codes <- read.csv("./Data/Postcodes.csv")
post_codes <- post_codes[ ,1]
coordinates <- read.csv("Postcodes coordinates.csv")

## install for newest verion of ggmap
#if(!requireNamespace("devtools")) install.packages("devtools")
#devtools::install_github("dkahle/ggmap", ref = "tidyup")

register_google(key = "")
```

## UK
90% of customers are located in the UK
```{r}
ggmap(get_map("Machester", zoom = 6, api_key = key), extent = "device") +
  geom_point(aes(x = lon, y = lat), data = coordinates,
             alpha = .15, color="darkred", size = 2)
```
## Greater London
60% of all customers are located in London
```{r}
ggmap(get_map("SW1W", zoom = 11, api_key = key), extent = "device") +
  geom_point(aes(x = lon, y = lat), data = coordinates,
             alpha = .15, color="darkred", size = 2)
```
## London
```{r}
ggmap(get_map("SW1W", zoom = 12, api_key = key), extent = "device")+
  geom_point(aes(x = lon, y = lat), data = coordinates,
             alpha = .2, color="darkred", size = 2)
```
## London 2
```{r}
ggmap(get_map("SW1W", zoom = 13, api_key = key), extent = "device")+
  geom_point(aes(x = lon, y = lat), data = coordinates,
             alpha = .25, color="darkred", size = 2)
```
## Sloane Square
```{r}
ggmap(get_map("Sloane Square", zoom = 14, api_key = key), extent = "device")+
  geom_point(aes(x = lon, y = lat), data = coordinates,
             alpha = .25, color="darkred", size = 2)
```

# Spending by Regions
```{r}
# $V1 takes it as a vector instead of data frame
London_regions <- read.csv("./Data/London Town and Boroughs.csv", stringsAsFactors = FALSE, header = FALSE)$V1

info_walk <- read.csv("./Data/info walk.csv", stringsAsFactors = FALSE)
Post_code_regex <- "^([A-PR-UWYZ0-9][A-HK-Y0-9][AEHMNPRTVXY0-9]?[ABEHMNPRVWXY0-9]? {1,2}[0-9][ABD-HJLN-UW-Z]{2}|GIR 0AA)$"

# Of all the data there are 300 blank postcodes/addresses of which 161 are paying customers
# counts <- data.frame(matrix(ncol = 5, nrow = 5))
# counts[,1] <- c("London", "UK", "UK ex. London", "Non-UK", "All")
# colnames(counts) <- c("Region", "No. customers", "Total Spend", "% of customers", "% of spend")


counts <- tibble(region = c("London", "UK", "UK ex. London", "Non-UK", "All"), 
                 Num_Customers = NA,
                 Total_Spend = NA,
                 Percentagee_Customers = NA,
                 Percentage_Spend = NA)

Total_spend <- unlist(info_walk %>%
  filter(Amount > 0, Post != "") %>%
  summarise(sum(Amount)))

Total_count <- unlist(info_walk %>%
  filter(Amount > 0, Post != "") %>%
  distinct(Name) %>%
  summarise(number = n()))

# gets London residents only
counts[1, -1] <- unlist(info_walk %>% 
  filter(grepl(Post_code_regex, Post), tolower(Town.x) %in% tolower(London_regions), Amount > 0) %>% 
  group_by(Name) %>% 
  summarise(Total.spend = sum(Amount, na.rm = TRUE)) %>% 
  summarise(number = n(), Sum.of.spend = round(sum(Total.spend))) %>% 
  mutate(percent.count = (number/Total_count)*100,
         percent.spend = (Sum.of.spend/Total_spend)*100))

# finds UK postcodes only
counts[2, -1] <- unlist(info_walk %>% 
  filter(grepl(Post_code_regex, Post) | tolower(Town.x) %in% tolower(London_regions), Amount > 0) %>%
  group_by(Name) %>% 
  summarise(Total.spend = sum(Amount, na.rm = TRUE)) %>% 
  summarise(number = n(), Sum.of.spend = round(sum(Total.spend))) %>%
  mutate(percent.count = (number/Total_count)*100,
         percent.spend = (Sum.of.spend/Total_spend)*100))

# finds UK, non-London postcodes
counts[3, -1] <- unlist(info_walk %>% 
  filter(grepl(Post_code_regex, Post), !(tolower(Town.x) %in% tolower(London_regions)), Amount > 0) %>%
  group_by(Name) %>% 
  summarise(Total.spend = sum(Amount, na.rm = TRUE)) %>% 
  summarise(number = n(), Sum.of.spend = round(sum(Total.spend))) %>%
  mutate(percent.count = (number/Total_count)*100,
         percent.spend = (Sum.of.spend/Total_spend)*100))

# find non UK postcodes
counts[4, -1] <- unlist(info_walk %>% 
  filter(!grepl(Post_code_regex, Post), Post != "", !(tolower(Town.x) %in% tolower(London_regions)), Amount > 0) %>%
  group_by(Name) %>% 
  summarise(Total.spend = sum(Amount, na.rm = TRUE)) %>% 
  summarise(number = n(), Sum.of.spend = round(sum(Total.spend))) %>%
  mutate(percent.count = (number/Total_count)*100,
         percent.spend = (Sum.of.spend/Total_spend)*100))

# The totals
counts[5, -1] <- c(Total_count, Total_spend, 100, 100)

write.csv(counts, "./Analysis/Customers by region.csv", row.names = FALSE)

select(counts, -c(Total_Spend, Num_Customers))
```

# Walking Distance from Store
```{r}
distance <- data.frame()

# $V1 takes it as a vector instead of data frame
London_regions <- read.csv("./Data/London Town and Boroughs.csv", stringsAsFactors = FALSE, header = FALSE)$V1
Post_code_regex <- "^([A-PR-UWYZ0-9][A-HK-Y0-9][AEHMNPRTVXY0-9]?[ABEHMNPRVWXY0-9]? {1,2}[0-9][ABD-HJLN-UW-Z]{2}|GIR 0AA)$"

London_cust <- info %>% 
  filter(grepl(Post_code_regex, Post), tolower(Town) %in% tolower(London_regions), Amount > 0) %>%
  distinct(Post)
```

```{r}
# getting the walking distance from Google
distance <- unnest(
  enframe(
    map(London_cust$Post, mapdist, to = "SW1W 1EX", mode = "walking", output = "simple")
    )
  )
info_walk <- inner_join(x = info, y = distance, by = c("Post" = "from"))
write.csv(distance, "./data/walking distance from store.csv", row.names = FALSE)

walking_dist <- read.csv("./data/walking distance from store.csv", stringsAsFactors = FALSE)
walking_dist <- subset(walking_dist, minutes < 300)
head(select(walking_dist, -from))
```

## Histogram of Walking Distance
```{r}
# ggplotly(
ggplot(walking_dist, aes(minutes)) +
  theme(axis.text.y=element_blank()) +
  geom_histogram(position = 'stack', binwidth = 5, alpha = 0.9, col = "white", boundary = 0)#,
#   width = 900
# )
```

## Cumulative Graph of Walking Distance from Store
Only 30% of customers live within a 30 minute walk from the store. Some may work nearby, or the store may be a destination store instead of relying on local clientel.
```{r}
ggplotly(
  ggplot(walking_dist %>% arrange(minutes), aes(minutes)) + 
    stat_ecdf(geom = "step", col = "blue", size = 0.5) +
    scale_x_continuous(breaks = seq(0, 300, 15)) +
    scale_y_continuous(breaks = seq(0, 1, 0.1)) +
    ylab("Percentage") +
    ggtitle("Walking Distance from Store"),
  width = 900
)
```

# Age Demographics Comparison
```{r}
# Categorises customer numbers and spending into age categories and filters by proximity of 45 min walk
age_summary <- function(lower, upper){
  info_walk %>% 
    group_by(Name) %>% 
    summarise(spend = sum(Amount, na.rm = TRUE), Age = Age[1], minutes = minutes[1]) %>%  # as each name has multiple only take 1st age&min
    filter(Age >= lower, Age <= upper, spend > 0, minutes > 30) %>% 
    summarise(count = n_distinct(Name))
}

# Census data
census <- read.csv("./Census data/Census area summary.csv", stringsAsFactors = FALSE)

TD_count <- mapply(age_summary, lower = census$Lower, upper = census$Upper)
TD_count <- matrix(unlist(t(TD_count)), ncol = 1)  #Transposes then converts the list to a matrix
colnames(TD_count) <- "TD_count"

census_sum <- cbind(census, TD_count)
census_sum <- mutate(census_sum, count_percent = TD_count/sum(TD_count))

age_group <- paste(census_sum[ ,1], "-", census_sum[ ,2], sep = "")
comparison <- cbind(age_group, census_sum[,3:6])
colnames(comparison) <- c("age_group", "Count", "Census", "TD_count", "TD customers")
comp_long <- cbind(age_group, gather(comparison[,2:5]))
comp_long <- subset(comp_long, key %in% c("Census", "TD customers"))
# keep data as factors, otherwise it sorts by numerical order
comp_long$age_group <- factor(comp_long$age_group, levels = unique(comp_long$age_group))
comp_long$key <- factor(comp_long$key, levels = unique(comp_long$key))
```
For age comparison it only looks at 160 out of 940 customer, after filtering for 45min walking proximity, age data and spending.
Overall compared to the local area the store attracts a much larger proportion of those aged between 45-59 compared to other ages groups.
```{r}
comp_long <- comp_long %>% mutate(percent = value*100)
# ggplotly(
  ggplot(comp_long, aes(x = age_group, y = percent, fill = key)) +
    theme(axis.text.x=element_blank()) +
    geom_bar(stat = "identity", position = position_dodge()) +
    xlab("Age Categories") +
    ylab("Percentage") +
    ggtitle("Comparison of Age Groups (2011 K&C Census vs Store)") +
    scale_y_continuous(breaks = seq(0, 100, 10))#,
#   width = 900,
#   height = 500
# )
```


